1. Technical Field
This disclosure relates to thematic classifications and more particularly to multi-instance learning systems that process high resolution spatial imagery.
2. Related Art
Land use is subject to rapid change. Change may occur because of urbanization, political conflicts, population displacements, and poverty. The unplanned, unauthorized, and/or unstructured homes, known as informal settlements, shantytowns, barrios, or slums, pose several challenges for nations. They may be located in hazardous regions and may lack basic services.
The use of satellite imagery has been ineffective in assessing change because the typical object size recorded in satellite imagery is much larger than the pixel resolution that renders the satellite imagery. A pixel by itself is not a good indicator of the objects it forms. Many per-pixel (single instance) based thematic classification schemes are good for analyzing medium and coarse resolution images. Thus, known learning approaches based on per-pixel spectral features are ineffective in high-resolution urban image classification.